skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

This content will become publicly available on March 1, 2020

Title: QP Statistics Spotlight: Practical versus Statistical Priorities

Abstract

Randomization during data collection in a designed experiment is an effective strategy for reducing system bias and ensuring independent observations1, but at what cost should it be fully implemented? We recently encountered a scenario that highlights an important aspect of collaboration2 between statisticians, scientists and engineers – the idea of compromise for the sake of achieving wins for all team members. In a multi-million-dollar experiment with a fixed amount of time on the test equipment, each experimental run involved resetting the input values and then waiting for the continuous process to reach a new equilibrium. For example, the temperature of the furnace could be reset, but it takes some time for the test chamber to reach the new temperature and rest at that temperature for a while until all components in the chamber settle at that new setting. Similarly, a critical gas flow component also required a settling period before data could be collected that would be representative of that input setting value. In all, the experiment involved 5 factors, where 4 of the 5 were time-consuming and costly to adjust, with the duration of time needed to reach new equilibrium being dependent on how far the previous setting wasmore » from the new one for the combination of these 4 factors. For example, changing from 150° to 200° would reach equilibrium faster than changing from 100° to 200°.« less

Authors:
ORCiD logo [1];  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of South Florida, Tampa, FL (United States). Dept. of Mathematics and Statistics
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1511255
Report Number(s):
LA-UR-18-31304
Journal ID: ISSN 0033-524X
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Quality Progress
Additional Journal Information:
Journal Volume: 52; Journal Issue: 3; Journal ID: ISSN 0033-524X
Publisher:
American Society for Quality Control
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Mathematics

Citation Formats

Anderson-Cook, Christine Michaela, and Lu, Lu. QP Statistics Spotlight: Practical versus Statistical Priorities. United States: N. p., 2019. Web.
Anderson-Cook, Christine Michaela, & Lu, Lu. QP Statistics Spotlight: Practical versus Statistical Priorities. United States.
Anderson-Cook, Christine Michaela, and Lu, Lu. Fri . "QP Statistics Spotlight: Practical versus Statistical Priorities". United States.
@article{osti_1511255,
title = {QP Statistics Spotlight: Practical versus Statistical Priorities},
author = {Anderson-Cook, Christine Michaela and Lu, Lu},
abstractNote = {Randomization during data collection in a designed experiment is an effective strategy for reducing system bias and ensuring independent observations1, but at what cost should it be fully implemented? We recently encountered a scenario that highlights an important aspect of collaboration2 between statisticians, scientists and engineers – the idea of compromise for the sake of achieving wins for all team members. In a multi-million-dollar experiment with a fixed amount of time on the test equipment, each experimental run involved resetting the input values and then waiting for the continuous process to reach a new equilibrium. For example, the temperature of the furnace could be reset, but it takes some time for the test chamber to reach the new temperature and rest at that temperature for a while until all components in the chamber settle at that new setting. Similarly, a critical gas flow component also required a settling period before data could be collected that would be representative of that input setting value. In all, the experiment involved 5 factors, where 4 of the 5 were time-consuming and costly to adjust, with the duration of time needed to reach new equilibrium being dependent on how far the previous setting was from the new one for the combination of these 4 factors. For example, changing from 150° to 200° would reach equilibrium faster than changing from 100° to 200°.},
doi = {},
journal = {Quality Progress},
number = 3,
volume = 52,
place = {United States},
year = {2019},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on March 1, 2020
Publisher's Version of Record
The DOI is not currently available

Save / Share: